Interview guides · Bertelsmann

Bertelsmann Data Scientist Interview Questions & Guide 2026

Case practice for Bertelsmann data scientist candidates: scope a vague prompt into a measurable question before you analyze it.

The 60-second brief

As a Data Scientist at Bertelsmann, you are stepping into a pivotal role within one of the world's largest and most diverse media, services, and education conglomerates. Your work will directly influence how data is leveraged across a massive portfolio of businesses, which includes RTL Group, Penguin Random House, BMG, and Arvato. You are not just building models; you are building the intelligence layer that drives strategic decisions across these varied divisions.

Scope your preparation by the data you would actually touch, because the title will not tell you. A seat that lives in event logs and weekly readouts rewards fluency in aggregation and metric definitions; a seat that owns a model in production rewards fluency in train/serve skew, retraining cadence and drift monitoring. The fastest way to find out which one you are interviewing for is to ask what the team shipped last quarter and what it gets paged about.

Focus areas

Power experiments for heavy-tailed account revenue
Read NRR on a fixed account cohort
Separate contracted seats from actively used seats

Overview

The impact of this position is vast. Whether you are optimizing supply chain logistics for Arvato, enhancing content personalization algorithms for streaming platforms, or predicting consumer trends for global publishing houses, your data-driven insights will shape user experiences and operational efficiencies. Bertelsmann relies on its data teams to bridge the gap between complex mathematical modeling and tangible business value.

You can expect a highly collaborative and dynamic environment. Because Bertelsmann operates across so many different sectors, a Data Scientist here must be adaptable, commercially aware, and capable of scaling solutions to meet enterprise-level demands. You will be tasked with navigating complex data ecosystems, translating ambiguous business problems into structured technical projects, and delivering solutions that have a measurable impact on the bottom line.

The Bertelsmann interview process

Bertelsmann candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening Call

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Core Interview Stages

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

Stakeholder Communication Exercise

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Technical Discussion

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Where candidates lose points

Comparing accounts that received a sales or customer-success touch against those that did not

Assignment of coverage is deliberate and pulls in both directions at once: the largest accounts get a named owner because they are valuable, and the accounts showing distress get one because they are at risk. The comparison therefore mixes a strong positive selection with a strong negative one, and the naive estimate can come out with either sign depending on which assignment rule dominated during the period examined. Nothing about matching on observed size fixes this, because the risk signal that triggered coverage is usually the same signal that predicts the outcome. It needs either an actual randomised or staggered rollout of coverage, or a design built on a capacity constraint or territory boundary that assigns coverage for reasons unrelated to account health.

Computing monthly churn against the entire customer base when contracts are annual

An annual contract has no opportunity to churn except at its renewal date, so an account that is eleven months from renewal is in the denominator while being incapable of appearing in the numerator. The resulting rate is smaller than the real one by roughly the ratio of the base to the renewal-eligible base, and it oscillates with the seasonality of when deals were originally signed rather than with anything about the customers. The corresponding trap on the other side is counting a churn on the date the record was updated rather than on term_end_date, which shifts losses into whichever month the operations team did its paperwork.

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

Naming a model class before naming the deployment constraints

Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.

Questions & practice

Explain the concept of a p-value to a marketing manager who has no background in statistics.

Medium · statistics and probability

Approach

Quantify uncertainty explicitly rather than reporting a point estimate alone.
Translate the result into the decision it informs, in one plain sentence.
Sanity-check the answer against a simple bound or a simulated case.

Follow-up

What sample size would you need to detect an effect half this size?
How would you explain this result to someone who does not know statistics?

Describe a situation where your model failed in production. How did you resolve it?

Medium · machine learning and modelling

Approach

Pick an evaluation metric that matches the cost of each error type, not a default.
Say how the offline result would be validated online before it is trusted.
Check what information would not exist at prediction time, and exclude it.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

How would you design an algorithm to personalize content recommendations for users on a streaming platform?

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Set a baseline first, so any model has something honest to beat.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

Where could label leakage enter this setup?
What would you monitor after launch to know the model is still valid?

Why did you choose [Specific Algorithm] over [Alternative Algorithm] in your previous role?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Pick an evaluation metric that matches the cost of each error type, not a default.
Say how the offline result would be validated online before it is trusted.

Follow-up

Where could label leakage enter this setup?
What would you monitor after launch to know the model is still valid?

Audit daily usage rows for grain and arithmetic violations

Easy · data-quality · pandas · grain

You are handed fct_usage_daily as a pandas DataFrame with account_id, workspace_id, sku_code, usage_date, billable_quantity, included_quantity_applied, overage_quantity, list_amount_cents, discount_amount_cents, net_amount_cents, cogs_cents, is_restated, first_written_at and restated_at. The declared grain is one row per (account_id, workspace_id, sku_code, usage_date). Write audit(df) returning a DataFrame with one row per failing check: check name, failing row count, and one example key. Cover at minimum grain duplication, negative quantities or amounts, the identity net = list - discount, billable = included + overage, and rows where is_restated is true but restated_at is null.

Approach

Check the grain before anything else with df.duplicated(subset=key, keep=False), and count rows rather than groups so a key appearing twice contributes 2 — if the grain is broken every arithmetic count below it is uninterpretable.
Express each invariant as a boolean Series over the whole frame. The cent columns are integers and compare exactly, so use !=; the numeric(18,6) quantity columns need np.isclose with atol=1e-6 because included + overage is a decimal sum.
Handle null as its own failure mode. Comparisons against NaN return False, so a check written as rows_that_pass = (a == b - c) silently files every null-amount row wherever the negation happens to land; build each check as violations = ~condition | column.isna().
Collect the checks as a list of (name, mask) pairs and assemble the output in one pass, so adding a check is one line and every check reports in the same shape.
Order the output with structural failures (grain, null keys) above arithmetic failures, and report zero-count checks too — a check that silently disappears when it passes is indistinguishable from a check that was never run.

Worked solution (20 min)

key = ['account_id','workspace_id','sku_code','usage_date']; dup = df.duplicated(key, keep=False); record dup.sum() and df.loc[dup, key].iloc[0].to_dict() as the example.
neg = (df[['billable_quantity','included_quantity_applied','overage_quantity','list_amount_cents','net_amount_cents','cogs_cents']] < 0).any(axis=1); net_bad = df.net_amount_cents.isna() | (df.net_amount_cents != df.list_amount_cents - df.discount_amount_cents).
qty_bad = ~np.isclose(df.billable_quantity, df.included_quantity_applied + df.overage_quantity, atol=1e-6) | df.billable_quantity.isna(); restated_bad = df.is_restated & df.restated_at.isna().
Assemble pd.DataFrame([{'check': n, 'failing_rows': int(m.sum()), 'example': first_key(m)} for n, m in checks]) with the grain and null checks listed first.

Expected result: One row per check with failing_rows as an integer count of rows rather than groups, and a JSON-serialisable example key for any check with a non-zero count; a single doubled key contributes 2 to the grain check.

Verify it yourself

df.groupby(key).size().max() == 1 if and only if the grain check reports zero.
When the net check reports zero, sum(net_amount_cents) == sum(list_amount_cents) - sum(discount_amount_cents) exactly.
Running audit on df.sample(frac=1, random_state=0) gives identical counts; the checks must not depend on row order.
Corrupting one net_amount_cents by a single cent raises exactly one check's count by exactly 1.

Follow-up

Which of these should block a dashboard refresh and which should only warn?
Rows with is_restated = true legitimately change value after first write. How do you make yesterday's audit result reproducible?
How would you extend this to catch a partition that is missing entirely rather than wrong?

Seven-day activation rate by signup cohort week

Easy · funnel · cohorts · censoring

dim_account carries account_id, created_at, is_internal and is_current; fct_api_request carries account_id, request_at, http_status, api_key_id and traffic_class. Build a signup cohort by the ISO week of created_at over accounts with is_internal = false. An account counts as activated when it issues a request with http_status < 400, a non-null api_key_id and traffic_class <> 'synthetic_monitor' within 168 hours of its own created_at. Return cohort accounts, activated accounts and the rate per week, and exclude any week that has not yet fully elapsed its 168-hour window.

Approach

Collapse dim_account to one row per account_id before anything else. It is a type 2 dimension, so a plan or status change gives the same account several rows; filtering to is_current = true is the cheapest correct choice here because created_at does not change across versions.
Express the window as interval arithmetic on the timestamptz column: request_at >= created_at AND request_at < created_at + interval '168 hours'. A date-difference of 7 days is a different and wrong condition for accounts created mid-day.
Test activation with EXISTS rather than a join to MIN(request_at). EXISTS short-circuits, keeps the cohort at one row per account, and cannot fan out.
Aggregate by date_trunc('week', created_at AT TIME ZONE 'UTC'), counting accounts and activated accounts, and divide as a ratio of counts.
Drop unreportable weeks: the last account in a cohort week is created just under week_start + 7 days, so the week is only complete once now() >= week_start + interval '14 days'. Without that filter the newest week always looks like a regression.

Worked solution (20 min)

Write the cohort CTE: SELECT account_id, created_at FROM dim_account WHERE is_current AND NOT is_internal AND created_at >= <window start>.
Add the activation predicate as a correlated EXISTS over fct_api_request on account_id with the four conditions: http_status < 400, api_key_id IS NOT NULL, traffic_class <> 'synthetic_monitor', and the 168-hour bracket.
Group by date_trunc('week', created_at AT TIME ZONE 'UTC'); select count() AS cohort_accounts, count() FILTER (WHERE activated) AS activated_accounts, and the ratio cast to numeric.
Add HAVING or an outer WHERE that keeps only weeks where week_start + interval '14 days' <= now().
Spot-check one account that activated on hour 167 and one that activated on hour 169 to confirm the boundary is exclusive at the top.

Expected result: One row per ISO cohort week with cohort_accounts, activated_accounts and activation_rate between 0 and 1; the newest row is at least 14 days old, and accounts with no rows at all in fct_api_request appear in the denominator with activated = 0 rather than disappearing.

Verify it yourself

Summing cohort_accounts across weeks equals the count of non-internal current-version accounts created in the same window.
Re-running the query a day later leaves every previously reported week byte-identical; any movement means the censoring filter is wrong.
activated_accounts is never greater than cohort_accounts, and the count of distinct account_id in the activated set matches a separate COUNT DISTINCT over the fact table.

Follow-up

The median time-to-first-successful-call is more informative than a fixed-window rate. Why can you not compute it from this query, and what estimator does it need?
How would you separate accounts that never called from accounts that called and got only 4xx responses, and which of those is a product problem?

Find paid seats that never called the API

Easy · anti-join · null semantics · seat utilisation

dim_user_membership carries user_id, account_id, seat_type, is_service_account, deactivated_at and last_seen_at. fct_api_request carries user_id, which is null whenever the caller is a service account or an unattended key, plus account_id, request_at and http_status. For a single account, list every licensed_paid seat with is_service_account = false and deactivated_at null that issued no request at all in the trailing 90 days, returning user_id and last_seen_at. A first draft writes NOT IN against a subquery over fct_api_request.user_id. State exactly what that draft returns and why, then write the correct query.

Approach

Read the column comment first: user_id is nullable on the fact, so the subquery almost certainly contains at least one NULL for any account that runs a service account or an unattended key.
Work the three-valued logic out loud. x NOT IN (a, NULL) expands to NOT (x = a OR x = NULL); the second disjunct is UNKNOWN, so for any x not equal to a the whole predicate is UNKNOWN and the row is filtered out. The draft returns zero rows, which reads as full seat utilisation.
Rewrite as NOT EXISTS with the 90-day and status predicates inside the correlated subquery. Putting them in the outer WHERE instead turns the anti-join into a different question and silently changes the answer.
Correlate on both user_id and account_id. A membership is (user_id, account_id) and one person can hold memberships in several accounts, so correlating on user_id alone marks a seat as active because that human was busy somewhere else.
Filter the seat side to seat_type = 'licensed_paid', is_service_account = false and deactivated_at IS NULL, and say what the resulting count means next to contracted_seats on the current subscription row.

Follow-up

Adding AND user_id IS NOT NULL to the subquery also fixes NOT IN. Why is NOT EXISTS still the form you would leave in the repository?
last_seen_at looks like a shortcut for the whole query. What does it actually record, and where does it disagree with API activity?
This is a seat-reduction risk list. What threshold would you attach before handing it to an account team, and what happens to seats below it?

Explain how you would set up an A/B test to measure the impact of a new feature.

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
Name the randomisation unit first; it decides the variance and what the test can detect.
Say whether units interfere with each other, and switch design if they do.

Follow-up

What would you do if you could not randomise at all?
How would you handle interference between treated and control units?

What is the difference between bagging and boosting?

Medium

Approach

State your assumptions explicitly before working the problem.
Clarify what is being asked and what a complete answer would contain.
Work from the decision backwards to the evidence you would need.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Size an account-randomised test of a consumption feature

Easy · power · mde · heavy-tailed-outcomes

You have 2,800 non-internal paying accounts. The outcome is the monthly sum of billable_quantity for sku_code = 'compute_hours' from fct_usage_daily, with a mean of 640 and a standard deviation of 2,100 across accounts. Product wants to detect a 5% lift from a scheduling feature, randomised 50/50 at the account level, at 80% power and two-sided alpha 0.05. Give the minimum detectable effect the available sample actually supports, say whether the 5% target is reachable, and state what you would change in the design or the metric before agreeing to run it.

Approach

Write the two-sample formula and plug it before discussing anything else: MDE = (z_0.975 + z_0.80) * sd * sqrt(1/n1 + 1/n2) = 2.80 * 2100 * sqrt(2/1400) = 222 compute-hours, which is 34.7% of the 640 mean. The driver is the coefficient of variation of 3.3, not the account count, so quoting a sample size without it is not a power calculation.
Invert for the stated target. A 5% lift is 32 compute-hours, needing N = 4 * (2.80 * 2100 / 32)^2 = about 135,000 accounts, roughly 48 times the eligible population. Say the test is not powerable on this metric, and do not offer to run it longer: the account population is fixed, and extra weeks add repeated observations on the same clusters rather than new clusters.
Attack the variance instead of the sample. Pre-register winsorisation at the 99th percentile of the pre-period distribution, or move the estimand to a bounded one such as the share of accounts exceeding a usage floor. State explicitly that this changes the estimand rather than cleaning the data, because a cap chosen after seeing the result encodes the answer.
Add CUPED on the account's pre-period compute_hours. At a pre/post correlation of 0.80 the residual variance multiplier is 1 - 0.80^2 = 0.36, the standard error falls to 0.60 of its unadjusted value, and the MDE drops from 222 to 133 compute-hours (20.8% relative). That is still four times the requested 5%.
Fix the randomisation unit and the balance in the same breath: randomise account_id because colleagues share workspaces, and stratify assignment on pre-period usage decile so that a handful of very large accounts cannot land disproportionately in one arm and dominate the difference in means.
Close with an operating decision rather than a number. Run only if the team will act on a roughly 20% MDE after CUPED and winsorisation; otherwise re-specify the primary outcome as a bounded indicator and treat compute-hours as a secondary reported with intervals only.

Worked solution (25 min)

Compute the coefficient of variation, 2100 / 640 = 3.28, and check the top-decile share of total compute_hours before treating the mean as a summary of anything.
Compute MDE = 2.80 * 2100 * sqrt(1/1400 + 1/1400) = 2.80 * 2100 * 0.0378 = 222 compute-hours, 34.7% relative.
Invert to the required N for a 32-hour effect: 4 * (5883 / 32)^2 = about 135,000 accounts, 48x the population.
Recompute under CUPED at rho = 0.80: variance multiplier 0.36, SE multiplier 0.60, MDE 133 compute-hours, 20.8% relative.
Recompute on the binary variant at base rate 0.46 and write the recommendation as one decision with one operating point.

Expected result: MDE is about 222 compute-hours (34.7% relative) as specified and about 133 (20.8%) with CUPED at rho = 0.80. A 5% lift would require roughly 135,000 accounts, about 48 times the eligible population, so the test is declined on this metric and re-specified on a bounded outcome where the MDE falls to about 5.3 percentage points on a 0.46 base.

Verify it yourself

sqrt(1/1400 + 1/1400) = 0.0378, so the MDE must land within a few percent of 2.80 * 2100 * 0.0378 = 222. A materially different number means a one-sample or one-sided formula was used.
Required N scales with the inverse square of the effect size: halving the target MDE must quadruple N. If it does not, the formula is wrong.
The CUPED variance multiplier is 1 - rho^2, never 1 - rho. At rho = 0.80 the standard error falls by 40%, not by 20%.

Follow-up

The feature only applies to the 310 accounts on committed_consumption contracts. What is the MDE on that subset, and does it change the recommendation?
Suppose the primary outcome becomes a binary indicator, account exceeded 100 compute-hours in the month, at a base rate of 0.46. What is the MDE now, and why did it improve so much?
What would make you stop this test early, and how do you write that rule so it is not peeking?

Consumption per account always dips in the last four days

Easy · pipeline lag · restatement · reporting windows

Your consumption dashboard reads billable units per paying account from fct_usage_daily (account_id, sku_code, usage_date, billable_quantity, net_amount_cents, is_restated, first_written_at, restated_at, updated_at). Every refresh shows the final three to five days declining, and a review is scheduled on that shape. Establish empirically how long a usage_date takes to settle, separately per sku_code, and specify the trailing exclusion window every reported figure should use. Derive the number from the data; do not adopt a convention.

Approach

Treat settlement as a measurable curve rather than a belief: for each historical usage_date, compare the total captured as of age d days against that date's final settled total.
Do it per sku_code, because metering paths differ. A per-request SKU lands within hours, while a storage-month SKU is produced by a daily sweep and lands later, so one global lag number is wrong for at least one of them.
Pick the age at which a stated percentile of dates reaches a stated completeness threshold, for example the tenth percentile date reaching 99.5 percent of final, and take the slowest SKU's age as the dashboard's exclusion window.
Separate late arrival from restatement. Late rows raise totals and are fixed by waiting; restatements can move either way and are not, so report the share of the gap from each.
Encode the exclusion inside the query rather than in the chart, so anyone reusing the SQL inherits the rule, and never compare a fresh partial period against a settled one.

Follow-up

Month-end invoicing needs a number before settlement completes. How would you publish an early estimate with an honest uncertainty band attached?
What monitor would tell you the settling time has changed, without anyone remembering to re-run this analysis?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

Explaining a method to someone who will not follow the maths

Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.

Describe a time you had to push back on a stakeholder's request. How did you handle it?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Pick a story where you drove the decision, not one where you observed it.
Quantify the outcome, including what you would not claim credit for.

Follow-up

What did you decide not to do, and why?
What would you do differently if you ran that project again?

Tell me about a time you had to clean and prepare a particularly messy dataset.

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
State the situation in two sentences and spend the rest on your reasoning.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

How did you know the outcome was caused by your change?
What would you do differently if you ran that project again?

Walk through an analysis you later discovered was wrong

Easy · data quality · error ownership · metering

Six weeks ago you reported that consumption fell 9 percent in the last week of the month, and a team spent a sprint investigating the cause. The fall was an artefact: rows in fct_usage_daily land late and are restated in place, and you queried before the tail had settled. Describe how you found the error, what you told the people who acted on it, and the control you put in place so this class of mistake cannot reach a dashboard again. Be specific about how the settling window was measured.

Approach

The interviewer is probing whether you self-report errors before someone else finds them, and whether your fix is structural rather than a promise to be more careful. Say plainly that the number was wrong and that a sprint was spent on it, before describing any diagnosis.
Establish the artefact quantitatively instead of asserting that data lands late. For each usage_date, compare the total as of first_written_at against the settled total and read the settling time off that curve, for example 97 percent of final by day three and 99.5 percent by day five.
Correct the record the same day, in the channel the original number went out in, to the same audience. The cost of the wasted sprint belongs in the correction, not in a footnote.
Make the fix structural: exclude a trailing lag window from every reportable figure, and make the reporting view return no rows inside that window rather than returning partial ones. A dashboard that shades unsettled days still gets read as a decline.
State what generalises. Any fact table restated in place has this failure mode, so the guard belongs at the source rather than on the one dashboard that embarrassed you. A strong answer ends with the class of error closed; a generic one ends with a lesson learned.

Follow-up

How did you choose the completeness threshold behind the lag window, and what would make you recalibrate it?
What did you say to the team that lost the sprint, and what did they say back?
Is there a legitimate case for showing the unsettled tail at all, and to whom?